无监督学习新方法，提升坐姿识别在不同数据集间的鲁棒性
Unsupervised Dataset Dictionary Learning for domain shift robust clustering: application to sitting posture identification
- 基于Wasserstein均值对齐多源数据分布，实现无监督聚类
- 在Office31数据集上聚类对齐准确率显著提升
- 适合跨域坐姿识别场景，无需标注数据
本文提出一种全新的无监督数据字典学习方法（U-DaDiL），用于解决坐姿识别中因领域偏移导致的聚类不稳健问题。传统方法在不同数据集间适应性差，易受分布差异影响。U-DaDiL通过基于Wasserstein均值的表示对齐，有效融合多源数据分布特征。在Office31数据集上的实验表明，该方法显著提升了聚类对齐精度，为无监督坐姿识别中的领域鲁棒性提供了一条可行路径。
原文摘要 · Abstract (English)
This paper introduces a novel approach, Unsupervised Dataset Dictionary Learning (U-DaDiL), for totally unsupervised robust clustering applied to sitting posture identification. Traditional methods often lack adaptability to diverse datasets and suffer from domain shift issues. U-DaDiL addresses these challenges by aligning distributions from different datasets using Wasserstein barycenter based representation. Experimental evaluations on the Office31 dataset demonstrate significant improvements in cluster alignment accuracy. This work also presents a promising step for addressing domain shift and robust clustering for unsupervised sitting posture identification
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